SoroushVahidi/scidocs huggingface dataset:
A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums
(2012)

In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule.
Trust as a facilitator in cloud computing: a survey
(2012)

Cloud computing offers massively scalable, elastic resources (e.g., data, computing power, and services) over the internet from remote data centres to the consumers. The growing market penetration, with an evermore diverse provider and service landscape, turns Cloud computing marketplaces a highly competitive one. In this highly competitive and distributed service environment, the assurances are insufficient for the consumers to identify the dependable and trustworthy Cloud providers. This paper provides a landscape and discusses incentives and hindrances to adopt Cloud computing from Cloud consumers’ perspective. Due to these hindrances, potential consumers are not sure whether they can trust the Cloud providers in offering dependable services. Trust-aided unified evaluation framework by leveraging trust and reputation systems can be used to assess trustworthiness (or dependability) of Cloud providers. Hence, cloud-related specific parameters (QoS + ) are required for the trust and reputation systems in Cloud environments. We identify the essential properties and corresponding research challenges to integrate the QoS + parameters into trust and reputation systems. Finally, we survey and analyse the existing trust and reputation systems in various application domains, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) why trust establishment is important in the Cloud computing landscape, 2) how trust can act as a facilitator in this context and 3) what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.
Closing loops without places
(2010)

This paper proposes a new topo-metric representation of the world based on co-visibility that simplifies data association and improves the performance of appearance-based recognition. We introduce the concept of dynamic bagof-words, which is a novel form of query expansion based on finding cliques in the landmark co-visibility graph. The proposed approach avoids the - often arbitrary - discretisation of space from the robot's trajectory that is common to most image-based loop closure algorithms. Instead we show that reasoning on sets of co-visible landmarks leads to a simple model that out-performs pose-based or view-based approaches. Using real and simulated imagery, we demonstrate that dynamic bag-of-words query expansion can improve precision and recall for appearance-based localisation.
Design and Analysis of DC Solid-State Circuit Breakers Using SiC JFETs
(2016)

Protection against short-circuit faults remains a major technical challenge in increasingly popular dc power networks. This paper describes a new concept of a self-powered dc solid-state circuit breaker (SSCB) with one or more normally on SiC JFETs as the main static switch and a fast-starting isolated dc/dc converter as the protection driver. The new SSCB detects short-circuit faults by sensing its terminal voltage rise and draws power from the fault condition itself to turn and hold off the SiC switch. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or extra wiring. A low-power isolated dc/dc converter is designed and optimized to provide a fast reaction to a short-circuit fault. Unidirectional and bidirectional SSCB prototypes based on this design concept have been built. Repeated interruption of fault currents up to 180 A at a dc bus voltage of 400 V within 0.8 μs was experimentally demonstrated. DC circuit protection applications provide a unique market opportunity for wide-bandgap semiconductors, which are outside the conventional focus on power electronic converters.
Vision for Mobile Robot Navigation: A Survey
(2002)

This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.
A model-based approach to security flaw detection of network protocol implementations
(2008)

A lot of efforts have been devoted to the analysis of network protocol specification for reliability and security properties using formal techniques. However, faults can also be introduced during system implementation; it is indispensable to detect protocol implementation flaws, yet due to the black-box nature of protocol implementation and the unavailability of protocol specification most of the approaches resort to random or manual testing. In this paper we propose a model-based approach for security flaw detection of protocol implementation with a high fault coverage, measurability, and automation. Our approach first synthesizes an abstract behavioral model from a protocol implementation and then uses it to guide the testing process for detecting security and reliability flaws. For protocol specification synthesis we reduce the problem a trace minimization with a finite state machine model and an efficient algorithm is presented for state space reduction. Our method is implemented and applied to real network protocols. Guided by the synthesized model our testing tool reveals a number of unknown reliability and security issues by automatically crashing the implementations of the Microsoft MSN instant messaging (MSNIM) protocol. Analytical comparison between our model-based and prevalent syntax-based flaw detection schemes is also provided with the support of experimental results.
Code analysis of lightweight encryption algorithms using in RFID systems to improve cipher performance
(2012)

This research has investigated the performance analysis of lightweight encryption algorithms, which are using in RFID applications. In the first part, the codes of the investigated ciphers are analyzed, and then, four encryption algorithms include TEA, HIGHT, KATAN, and KLEIN are implemented on AVR Microcontroller to evaluate the number of CPU clock cycle for each cipher. In the last part, the results of two previous parts are compared.
Learning to Sketch with Shortcut Cycle Consistency
(2018)

To see is to sketch - free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process. This is an extremely challenging task because the photo and sketch domains differ significantly. Furthermore, human sketches exhibit various levels of sophistication and abstraction even when depicting the same object instance in a reference photo. This means that even if photo-sketch pairs are available, they only provide weak supervision signal to learn a translation model. Compared with existing supervised approaches that solve the problem of D(E(photo)) â†’ sketch), where E(Â·) and D(Â·) denote encoder and decoder respectively, we take advantage of the inverse problem (e.g., D(E(sketch) â†’ photo), and combine with the unsupervised learning tasks of within-domain reconstruction, all within a multi-task learning framework. Compared with existing unsupervised approaches based on cycle consistency (i.e., D(E(D(E(photo)))) â†’ photo), we introduce a shortcut consistency enforced at the encoder bottleneck (e.g., D(E(photo)) â†’ photo) to exploit the additional self-supervision. Both qualitative and quantitative results show that the proposed model is superior to a number of state-of-the-art alternatives. We also show that the synthetic sketches can be used to train a better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively alleviating the problem of sketch data scarcity.
Design and Experimental Results of a 300-kHz Synthetic Aperture Sonar Optimized for Shallow-Water Operations
(2009)

The design of a shallow-water synthetic aperture sonar (SAS) requires an understanding of key system and environmental issues. The main factors that limit SAS performance are as follows: micronavigation accuracy, where micronavigation is defined as the problem of estimating the acoustic path lengths to allow the focusing of the aperture; multipath effects; and target view angle changes. All of them can degrade shadow classification performance. Micronavigation accuracy is successfully addressed by the gyrostabilized displaced phase center antenna technique, which combines data-driven motion estimates with external attitude sensors. Multipath effects in shallow water are effectively countered by narrow vertical beams. Shadow blur induced by view angle changes is mitigated by increasing the center frequency, to reduce the SAS integration length while still maintaining the desired resolution and by designing the system with a minimum grazing angle of about 6deg to reduce shadow length. The combination of these factors led to the choice of a 300-kHz center frequency and of a multipath mitigation scheme that uses multiple vertical beams. Experimental results obtained with a sonar incorporating these features have produced SAS images with 1.6 cm times 5 cm resolution in range times cross-range and shadow contrast in excess of 5 dB, at ranges of up to 170 m in 20 m of water.
Graphical Figure Classification Using Data Fusion for Integrating Text and Image Features
(2013)

This paper describes a multimodal (image + text) learning approach for automatically identifying three graphical figure types commonly found in biomedical literature, namely, diagrams, statistical figures and flow charts. The goal is to improve retrieval of figures from biomedical journal articles. In this article, we describe a data fusion approach to combine information from both text and image sources, believed to contain complementary information. Text information about the image is extracted from the figure caption. The data fusion process includes a hybrid of evolutionary algorithm (EA) and Binary Particle Swarm Optimization (BPSO) called method applied to find an optimal subset of extracted image features. Chi-square statistic and information gain metric are used to select the optimal subset of extracted text features, which along with image features are input to Multi-Layer Perceptron Neural Network classifiers, whose outputs are characterized as fuzzy sets to determine the final classification result. Evaluation performed on 1707 figure images extracted from a test subset of Biome Central® journals extracted from U.S. National Library of Medicine's PubMed Central ® repository yielded classification accuracy as high as 96.1%.
Air-light estimation using haze-lines
(2017)

Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient.
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
(2011)

KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
(2010)

RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment applications will become available at cost below $100. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. The robotics and computer vision communities have developed a variety of techniques for 3D mapping based on laser range scans [8, 11], stereo cameras [7], monocular cameras [3], and unsorted collections of photos [4]. While RGB-D cameras provide the opportunity to build 3D maps of unprecedented richness, they have drawbacks that make their application to 3D mapping difficult: They provide depth only up to a limited distance (typically less than 5m), depth values are much noisier than those provided by laser scanners, and their field of view (∼ 60◦) is far more constrained than that of specialized cameras or laser scanners typically used for 3D mapping (∼ 180◦). In our work, we use a camera developed by PrimeSense [10]. The key insights of this investigation are: first, that existing frame matching techniques are not sufficient to provide robust visual odometry with these cameras; second, that a tight integration of depth and color information can yield robust frame matching and loop closure detection; third, that building on best practice techniques in SLAM and computer graphics makes it possible to build and visualize accurate and extremely rich 3D maps with such cameras; and, fourth, that it will be feasible to build complete robot navigation and interaction systems solely based on cheap depth cameras.
Time-of-Flight sensor calibration for accurate range sensing
(2010)

Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToFcameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data. 2010 Elsevier Inc. All rights reserved.
CALIBRATION OF A PMD-CAMERA USING A PLANAR CALIBRATION PATTERN TOGETHER WITH A MULTI-CAMERA SETUP
(2008)

We discuss the joint calibration of novel 3D range cameras based on the time-of-flight principle with the Photonic Mixing Device (PMD) and standard 2D CCD cameras. Due to the small field-of-view (fov) and low pixel resolution, PMD-cameras are difficult to calibrate with traditional calibration methods. In addition, the 3D range data contains systematic errors that need to be compensated. Therefore, a calibration method is developed that can estimate full intrinsic calibration of the PMD-camera including optical lens distortions and systematic range errors, and is able to calibrate the external orientation together with multiple 2D cameras that are rigidly coupled to the PMD-camera. The calibration approach is based on a planar checkerboard pattern as calibration reference, viewed from multiple angles and distances. By combining the PMD-camera with standard CCD-cameras the internal camera parameters can be estimated more precisely and the limitations of the small fov can be overcome. Furthermore we use the additional cameras to calibrate the systematic depth measurement error of the PMD-camera. We show that the correlation between rotation and translation estimation is significantly reduced with our method.
Learning Linear Threshold Functions in the Presence of Classification Noise
(1994)

I show that the linear threshold functions are polynomially learnable in the presence of classification noise, i.e., polynomial in <italic>n</italic>, 1/ε, 1/δ, and 1/&sgr;, where <italic>n</italic> is the number of Boolean attributes, ε and δ are the usual accuracy and confidence parameters, and &sgr; indicates the minimum distance of any example from the target hyperplane, which is assumed to be lower than the average distance of the examples from any hyperplane. This result is achieved by modifying the Perceptron algorithm—for each update, a weighted average of a sample of misclassified examples and a correction vector is added to the current weight vector. Similar modifications are shown for the Weighted Majority algorithm. The correction vector is simply   the mean of the normalized examples. In the special case of Boolean threshold functions, the modified Perceptron algorithm performs <italic>O</italic> (<italic>n</italic><supscrpt>2</supscrpt>ε<supscrpt>−2</supscrpt> ) iterations over <italic>O</italic>(<italic>n</italic><supscrpt>4</supscrpt>ε<supscrpt> −2</supscrpt>ln(<italic>n</italic>/(δε))) examples. This improves on the previous classification-noise result of Angluin and Laird to a much larger concept class with a similar number of examples, but with multiple iterations over the examples.
Usability Evaluation of Google Classroom : Basis for the Adaptation of GSuite E-Learning Platform
(2017)

Electronic learning is a technology learning that plays an important role in modern education and training. Its great contribution lies in the fact that content is available at any place and device from a fixed device to mobile device. Nowadays, education is accessible everywhere with the use of technology. There are several LMS (Learning Management Systems) available. One of the new tool available was released by Google under GSuite. Pangasinan State University is currently subscribed to GSuite for Education, and recently Google introduces Classroom as an eLearning platform for an educational institution. This research aims to evaluate the new product, its functionalities for the purpose of adapting and deployment. The main objective of this paper is to identify the usability and evaluation of the Learning Management System (LMS) Google Classroom, its functionalities, features, and satisfaction level of the students. Based on the result, the respondents agreed that GSuite classroom is recommended. The result of this study will be the proposed e-learning platform for Pangasinan State University, Lingayen Campus initially need in the College of Hospitality Management, Business and Public Administration.
Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN
(2017)

Chest radiography (chest X-ray) is a low-cost yet effective and widely used medical imaging procedures. The lacking of qualified radiologist seriously limits the applicability of the technique. We explore the possibility of designing a computer-aided diagnosis for chest X-rays using deep convolutional neural networks. Using a real-world dataset of 16,000 chest X-rays with natural language diagnosis reports, we can train a multi-class classification model from images and preform accurate diagnosis, without any prior domain knowledge.
Automatic detection of discontinuities from 3D point clouds for the stability analysis of jointed rock masses
(2012)

We describe an strategy for automatic detection of discontinuities in jointed rock masses from a 3D point cloud. The method consists on a sequence of processes for feature extraction and characterization of the discontinuities that use voxelization, robust detection of planes by sequential RANSAC and clustering. The results obtained by applying this methodology to the 3D point cloud are a set of discontinuity families, characterized by their dips and dip directions, and surface wedges. This geometric information is essential for a later stability analysis of the jointed rock mass. The ultimate aim of our investigation is the automatic extraction of discontinuity families for stability analysis of tunnels excavated in jointed rock masses.
Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification
(2016)

RSSI-based indoor localization is getting much attention. Thanks to a number of researchers, the localization accuracy has already reached a sufficient level. However, it is still not easy-to-use technology because of its heavy installation cost. When an indoor localization system is installed, it needs to collect RSSI data for training classifiers. Existing techniques need to collect enough data at each location. This is why the installation cost is very heavy. We propose a technique to gather data efficiently by using machine learning techniques. Our proposed algorithm is based on multi-task learning and Bayesian optimization. This algorithm can remove the need to collect data of all location labels and select location labels to acquire new data efficiently. We verify this algorithm by using a Wi-Fi RSSI dataset collected in a building. The empirical results suggest that the algorithm is superior to an existing algorithm applying single-task learning and Active Class Selection.